Nemo Curator

NeMo Curator - GPU-Accelerated Data Curation

NVIDIA's toolkit for preparing high-quality training data for LLMs.

When to use NeMo Curator

Use NeMo Curator when:

• Preparing LLM training data from web scrapes (Common Crawl)
• Need fast deduplication (16× faster than CPU)
• Curating multi-modal datasets (text, images, video, audio)
• Filtering low-quality or toxic content
• Scaling data processing across GPU cluster

Performance:

• 16× faster fuzzy deduplication (8TB RedPajama v2)
• 40% lower TCO vs CPU alternatives
• Near-linear scaling across GPU nodes

Use alternatives instead:

• datatrove: CPU-based, open-source data processing
• dolma: Allen AI's data toolkit
• Ray Data: General ML data processing (no curation focus)

Quick start

Installation

# Text curation (CUDA 12)
uv pip install "nemo-curator[text_cuda12]"

# All modalities
uv pip install "nemo-curator[all_cuda12]"

# CPU-only (slower)
uv pip install "nemo-curator[cpu]"

Basic text curation pipeline

from nemo_curator import ScoreFilter, Modify
from nemo_curator.datasets import DocumentDataset
import pandas as pd

# Load data
df = pd.DataFrame({"text": ["Good document", "Bad doc", "Excellent text"]})
dataset = DocumentDataset(df)

# Quality filtering
def quality_score(doc):
    return len(doc["text"].split()) > 5  # Filter short docs

filtered = ScoreFilter(quality_score)(dataset)

# Deduplication
from nemo_curator.modules import ExactDuplicates
deduped = ExactDuplicates()(filtered)

# Save
deduped.to_parquet("curated_data/")

Data curation pipeline

Stage 1: Quality filtering

from nemo_curator.filters import (
    WordCountFilter,
    RepeatedLinesFilter,
    UrlRatioFilter,
    NonAlphaNumericFilter
)

# Apply 30+ heuristic filters
from nemo_curator import ScoreFilter

# Word count filter
dataset = dataset.filter(WordCountFilter(min_words=50, max_words=100000))

# Remove repetitive content
dataset = dataset.filter(RepeatedLinesFilter(max_repeated_line_fraction=0.3))

# URL ratio filter
dataset = dataset.filter(UrlRatioFilter(max_url_ratio=0.2))

Stage 2: Deduplication

Exact deduplication:

from nemo_curator.modules import ExactDuplicates

# Remove exact duplicates
deduped = ExactDuplicates(id_field="id", text_field="text")(dataset)

Fuzzy deduplication (16× faster on GPU):

from nemo_curator.modules import FuzzyDuplicates

# MinHash + LSH deduplication
fuzzy_dedup = FuzzyDuplicates(
    id_field="id",
    text_field="text",
    num_hashes=260,      # MinHash parameters
    num_buckets=20,
    hash_method="md5"
)

deduped = fuzzy_dedup(dataset)

Semantic deduplication:

from nemo_curator.modules import SemanticDuplicates

# Embedding-based deduplication
semantic_dedup = SemanticDuplicates(
    id_field="id",
    text_field="text",
    embedding_model="sentence-transformers/all-MiniLM-L6-v2",
    threshold=0.8  # Cosine similarity threshold
)

deduped = semantic_dedup(dataset)

Stage 3: PII redaction

from nemo_curator.modules import Modify
from nemo_curator.modifiers import PIIRedactor

# Redact personally identifiable information
pii_redactor = PIIRedactor(
    supported_entities=["EMAIL_ADDRESS", "PHONE_NUMBER", "PERSON", "LOCATION"],
    anonymize_action="replace"  # or "redact"
)

redacted = Modify(pii_redactor)(dataset)

Stage 4: Classifier filtering

from nemo_curator.classifiers import QualityClassifier

# Quality classification
quality_clf = QualityClassifier(
    model_path="nvidia/quality-classifier-deberta",
    batch_size=256,
    device="cuda"
)

# Filter low-quality documents
high_quality = dataset.filter(lambda doc: quality_clf(doc["text"]) > 0.5)

GPU acceleration

GPU vs CPU performance

Multi-GPU scaling

from nemo_curator import get_client
import dask_cuda

# Initialize GPU cluster
client = get_client(cluster_type="gpu", n_workers=8)

# Process with 8 GPUs
deduped = FuzzyDuplicates(...)(dataset)

Multi-modal curation

Image curation

from nemo_curator.image import (
    AestheticFilter,
    NSFWFilter,
    CLIPEmbedder
)

# Aesthetic scoring
aesthetic_filter = AestheticFilter(threshold=5.0)
filtered_images = aesthetic_filter(image_dataset)

# NSFW detection
nsfw_filter = NSFWFilter(threshold=0.9)
safe_images = nsfw_filter(filtered_images)

# Generate CLIP embeddings
clip_embedder = CLIPEmbedder(model="openai/clip-vit-base-patch32")
image_embeddings = clip_embedder(safe_images)

Video curation

from nemo_curator.video import (
    SceneDetector,
    ClipExtractor,
    InternVideo2Embedder
)

# Detect scenes
scene_detector = SceneDetector(threshold=27.0)
scenes = scene_detector(video_dataset)

# Extract clips
clip_extractor = ClipExtractor(min_duration=2.0, max_duration=10.0)
clips = clip_extractor(scenes)

# Generate embeddings
video_embedder = InternVideo2Embedder()
video_embeddings = video_embedder(clips)

Audio curation

from nemo_curator.audio import (
    ASRInference,
    WERFilter,
    DurationFilter
)

# ASR transcription
asr = ASRInference(model="nvidia/stt_en_fastconformer_hybrid_large_pc")
transcribed = asr(audio_dataset)

# Filter by WER (word error rate)
wer_filter = WERFilter(max_wer=0.3)
high_quality_audio = wer_filter(transcribed)

# Duration filtering
duration_filter = DurationFilter(min_duration=1.0, max_duration=30.0)
filtered_audio = duration_filter(high_quality_audio)

Common patterns

Web scrape curation (Common Crawl)

from nemo_curator import ScoreFilter, Modify
from nemo_curator.filters import *
from nemo_curator.modules import *
from nemo_curator.datasets import DocumentDataset

# Load Common Crawl data
dataset = DocumentDataset.read_parquet("common_crawl/*.parquet")

# Pipeline
pipeline = [
    # 1. Quality filtering
    WordCountFilter(min_words=100, max_words=50000),
    RepeatedLinesFilter(max_repeated_line_fraction=0.2),
    SymbolToWordRatioFilter(max_symbol_to_word_ratio=0.3),
    UrlRatioFilter(max_url_ratio=0.3),

    # 2. Language filtering
    LanguageIdentificationFilter(target_languages=["en"]),

    # 3. Deduplication
    ExactDuplicates(id_field="id", text_field="text"),
    FuzzyDuplicates(id_field="id", text_field="text", num_hashes=260),

    # 4. PII redaction
    PIIRedactor(),

    # 5. NSFW filtering
    NSFWClassifier(threshold=0.8)
]

# Execute
for stage in pipeline:
    dataset = stage(dataset)

# Save
dataset.to_parquet("curated_common_crawl/")

Distributed processing

from nemo_curator import get_client
from dask_cuda import LocalCUDACluster

# Multi-GPU cluster
cluster = LocalCUDACluster(n_workers=8)
client = get_client(cluster=cluster)

# Process large dataset
dataset = DocumentDataset.read_parquet("s3://large_dataset/*.parquet")
deduped = FuzzyDuplicates(...)(dataset)

# Cleanup
client.close()
cluster.close()

Performance benchmarks

Fuzzy deduplication (8TB RedPajama v2)

• CPU (256 cores): 120 hours
• GPU (8× A100): 7.5 hours
• Speedup: 16×

Exact deduplication (1TB)

• CPU (64 cores): 8 hours
• GPU (4× A100): 0.5 hours
• Speedup: 16×

Quality filtering (100GB)

• CPU (32 cores): 2 hours
• GPU (2× A100): 0.2 hours
• Speedup: 10×

Cost comparison

CPU-based curation (AWS c5.18xlarge × 10):

• Cost: $3.60/hour × 10 = $36/hour
• Time for 8TB: 120 hours
• Total: $4,320

GPU-based curation (AWS p4d.24xlarge × 2):

• Cost: $32.77/hour × 2 = $65.54/hour
• Time for 8TB: 7.5 hours
• Total: $491.55

Savings: 89% reduction ($3,828 saved)

Supported data formats

• Input: Parquet, JSONL, CSV
• Output: Parquet (recommended), JSONL
• WebDataset: TAR archives for multi-modal

Use cases

Production deployments:

• NVIDIA used NeMo Curator to prepare Nemotron-4 training data
• Open-source datasets curated: RedPajama v2, The Pile

References

• [Filtering Guide](references/filtering.md) - 30+ quality filters, heuristics
• [Deduplication Guide](references/deduplication.md) - Exact, fuzzy, semantic methods

Resources

• GitHub: https://github.com/NVIDIA/NeMo-Curator ⭐ 500+
• Docs: https://docs.nvidia.com/nemo-framework/user-guide/latest/datacuration/
• Version: 0.4.0+
• License: Apache 2.0

Deduplication Guide

Complete guide to exact, fuzzy, and semantic deduplication.

Exact deduplication

Remove documents with identical content.

from nemo_curator.modules import ExactDuplicates

# Exact deduplication
exact_dedup = ExactDuplicates(
    id_field="id",
    text_field="text",
    hash_method="md5"  # or "sha256"
)

deduped = exact_dedup(dataset)

Performance: ~16× faster on GPU vs CPU

Fuzzy deduplication

Remove near-duplicate documents using MinHash + LSH.

from nemo_curator.modules import FuzzyDuplicates

fuzzy_dedup = FuzzyDuplicates(
    id_field="id",
    text_field="text",
    num_hashes=260,        # MinHash permutations (more = accurate)
    num_buckets=20,        # LSH buckets (more = faster, less recall)
    hash_method="md5",
    jaccard_threshold=0.8  # Similarity threshold
)

deduped = fuzzy_dedup(dataset)

Parameters:

• num_hashes: 128-512 (default 260)
• num_buckets: 10-50 (default 20)
• jaccard_threshold: 0.7-0.9 (default 0.8)

Performance: 16× faster on 8TB dataset (120h → 7.5h)

Semantic deduplication

Remove semantically similar documents using embeddings.

from nemo_curator.modules import SemanticDuplicates

semantic_dedup = SemanticDuplicates(
    id_field="id",
    text_field="text",
    embedding_model="sentence-transformers/all-MiniLM-L6-v2",
    embedding_batch_size=256,
    threshold=0.85,  # Cosine similarity threshold
    device="cuda"
)

deduped = semantic_dedup(dataset)

Models:

• all-MiniLM-L6-v2: Fast, 384 dims
• all-mpnet-base-v2: Better quality, 768 dims
• Custom models supported

Comparison

Best practices

1. Start with exact dedup - Remove obvious duplicates 2. Use fuzzy for large datasets - Best speed/quality trade-off 3. Semantic for high-value data - Expensive but thorough 4. GPU acceleration required - 10-16× speedup

Quality Filtering Guide

Complete guide to NeMo Curator's 30+ quality filters.

Text-based filters

Word count

from nemo_curator.filters import WordCountFilter

# Filter by word count
dataset = dataset.filter(WordCountFilter(min_words=50, max_words=100000))

Repeated content

from nemo_curator.filters import RepeatedLinesFilter

# Remove documents with >30% repeated lines
dataset = dataset.filter(RepeatedLinesFilter(max_repeated_line_fraction=0.3))

Symbol ratio

from nemo_curator.filters import SymbolToWordRatioFilter

# Remove documents with too many symbols
dataset = dataset.filter(SymbolToWordRatioFilter(max_symbol_to_word_ratio=0.3))

URL ratio

from nemo_curator.filters import UrlRatioFilter

# Remove documents with many URLs
dataset = dataset.filter(UrlRatioFilter(max_url_ratio=0.2))

Language filtering

from nemo_curator.filters import LanguageIdentificationFilter

# Keep only English documents
dataset = dataset.filter(LanguageIdentificationFilter(target_languages=["en"]))

# Multiple languages
dataset = dataset.filter(LanguageIdentificationFilter(target_languages=["en", "es", "fr"]))

Classifier-based filtering

Quality classifier

from nemo_curator.classifiers import QualityClassifier

quality_clf = QualityClassifier(
    model_path="nvidia/quality-classifier-deberta",
    batch_size=256,
    device="cuda"
)

# Filter low-quality (threshold > 0.5 = high quality)
dataset = dataset.filter(lambda doc: quality_clf(doc["text"]) > 0.5)

NSFW classifier

from nemo_curator.classifiers import NSFWClassifier

nsfw_clf = NSFWClassifier(threshold=0.9, device="cuda")

# Remove NSFW content
dataset = dataset.filter(lambda doc: nsfw_clf(doc["text"]) < 0.9)

Heuristic filters

Full list of 30+ filters:

• WordCountFilter
• RepeatedLinesFilter
• UrlRatioFilter
• SymbolToWordRatioFilter
• NonAlphaNumericFilter
• BulletsFilter
• WhiteSpaceFilter
• ParenthesesFilter
• LongWordFilter
• And 20+ more...

Best practices

1. Apply cheap filters first - Word count before GPU classifiers 2. Tune thresholds on sample - Test on 10k docs before full run 3. Use GPU classifiers sparingly - Expensive but effective 4. Chain filters efficiently - Order by cost (cheap → expensive)